Forecasting time series data using moving-window swarm intelligence-optimised machine learning regression

Ngo, Ngoc-Tri; Truong, Thi Thu Ha

doi:10.1504/ijiei.2019.103625

Cited by 5 publications

(3 citation statements)

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“…Firstly, there exist objects to be predicted, and each object is linked to its corresponding time to form an ordered sequence of data (random sequence), and this random sequence is described by applying a reasonably accurate mathematical model. At a later stage, this model can be identified, and after identification, the model can predict the future values of the sequence based on the past and present values of the sequence, which is the basic idea of the ARIMA model [17].…”

Section: Nonlinear Arima Model With Fused Feedback Svrmentioning

confidence: 99%

Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

2021

Journal of Mathematics

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In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.

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Section: Nonlinear Arima Model With Fused Feedback Svrmentioning

confidence: 99%

Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

2021

Journal of Mathematics

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“…It is used as an input for predicting the potential and future revenues of a company and it shows the capability of a firm to make a profit. In addition, when using intelligent systems to forecast time series data, such inputs prove useful (Ngo and Truong, 2019). Among the few studies that have paid attention to the companies' historical profitability trend, Amirthalingam and Balasundaram (2013) showed that the profitability of manufacturing companies in Sri Lanka, according to the financial reports of the selected companies, was not satisfying during 2006 to 2010.…”

Section: Related Workmentioning

confidence: 99%

Investigating the problem of profit margin decrease in manufacturing companies through SD simulation

Rabieh¹,

Shahzadi²,

Heydarbakian³

2021

IJIEI

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Manufacturing companies struggle with many constraints affecting their profitability. Profitability, as a financial performance indicator, is thus a concern for managers, policymakers, and stakeholders of all manufacturing companies. This study aims to address the problem of profit margin decrease in such companies. To that end, system dynamics (SD) simulation was used to address the problem in a comprehensive manner. Real-world data was obtained from a dairy company in Iran, and after going through the SD modelling steps, the validity of the model was proved based on the reproduction of the historical data. The results reveal that under low customer income and high raw material prices, the net profit margin decreases to -0.002 and -0.001, at its minimum, and had 14% and 1.2% average reduction yearly for years between 2018 and 2028. The results also reveal that the behaviour of the net profit margin could be improved by two proposed policies.

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“…Overestimation of energy usage results in unnecessary establishments and spinning reserves, inefficient load distributions, and an increase in the operation cost. In contrast, underestimation of consumption leads to insufficient reserves and high costs in the peaking unit, which limits economic and industrial development 1 . The building sector has considered the largest consumer of energy that accounts for nearly 40% of global energy usage and 33% of greenhouse gas emissions 2 .…”

Section: Introductionmentioning

confidence: 99%

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings

Ngo

Truong

et al. 2022

Sci Rep

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The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0–1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

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Forecasting time series data using moving-window swarm intelligence-optimised machine learning regression

Cited by 5 publications

References 0 publications

Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

Investigating the problem of profit margin decrease in manufacturing companies through SD simulation

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings

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